Automatic accurate craniofacial reconstruction based on geodesic statistical model

Abstract

Craniofacial reconstruction aims to estimate a person’s facial appearance from the skull and has applications in forensic investigation, reconstructive surgery, archaeology, and anthropology. The skull and face are typically represented as triangular meshes, on which geodesics provide intrinsic surface structures. Accordingly, we propose a craniofacial reconstruction framework that represents facial geometry using geodesics and learns a geodesic-based statistical model. First, we extract a set of geodesic curves for each registered face mesh, originating from the nose tip. Next, paired skull meshes and their corresponding facial geodesics are used to train a principal component analysis (PCA)-based statistical model that predicts the facial geodesics for an unseen skull. Finally, we reconstruct the full-face mesh by fitting a facial statistical shape model to the predicted geodesics. Because the intrinsic properties of the geodesics and geodesic distances are preserved under isometric deformations, the accuracy of the corresponding feature points automatically established by the geodesics and exact correspondence between the feature points are ensured, thereby improving the accuracy of the craniofacial reconstruction results. Experiments demonstrate improved reconstruction accuracy and reduced runtime compared with baseline methods.